School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory, Beijing 100074, China.
Sensors (Basel). 2022 Nov 9;22(22):8644. doi: 10.3390/s22228644.
We propose a robust RGB-Depth (RGB-D) Visual Odometry (VO) system to improve the localization performance of indoor scenes by using geometric features, including point and line features. Previous VO/Simultaneous Localization and Mapping (SLAM) algorithms estimate the low-drift camera poses with the Manhattan World (MW)/Atlanta World (AW) assumption, which limits the applications of such systems. In this paper, we divide the indoor environments into two different scenes: MW and non-MW scenes. The Manhattan scenes are modeled as a Mixture of Manhattan Frames, in which each Manhattan Frame in itself defines a Manhattan World of a specific orientation. Moreover, we provide a method to detect Manhattan Frames (MFs) using the dominant directions extracted from the parallel lines. Our approach is designed with lower computational complexity than existing techniques using planes to detect Manhattan Frame (MF). For MW scenes, we separately estimate rotational and translational motion. A novel method is proposed to estimate the drift-free rotation using MF observations, unit direction vectors of lines, and surface normal vectors. Then, the translation part is recovered from point-line tracking. In non-MW scenes, the tracked and matched dominant directions are combined with the point and line features to estimate the full 6 degree of freedom (DoF) camera poses. Additionally, we exploit the rotation constraints generated from the multi-view dominant directions observations. The constraints are combined with the reprojection errors of points and lines to refine the camera pose through local map bundle adjustment. Evaluations on both synthesized and real-world datasets demonstrate that our approach outperforms state-of-the-art methods. On synthesized datasets, average localization accuracy is 1.5 cm, which is equivalent to state-of-the-art methods. On real-world datasets, the average localization accuracy is 1.7 cm, which outperforms the state-of-the-art methods by 43%. Our time consumption is reduced by 36%.
我们提出了一种鲁棒的 RGB-Depth（RGB-D）视觉里程计（VO）系统，通过使用几何特征，包括点和线特征，来提高室内场景的定位性能。以前的 VO/同时定位与建图（SLAM）算法使用曼哈顿世界（MW）/亚特兰大世界（AW）假设来估计低漂移相机姿势，这限制了此类系统的应用。在本文中，我们将室内环境分为两种不同的场景：MW 和非-MW 场景。MW 场景被建模为一个混合曼哈顿框架，其中每个曼哈顿框架本身定义了一个特定方向的曼哈顿世界。此外，我们提供了一种使用从平行线提取的主导方向检测曼哈顿框架（MF）的方法。我们的方法设计的计算复杂度低于使用平面检测曼哈顿框架（MF）的现有技术。对于 MW 场景，我们分别估计旋转和平移运动。提出了一种新方法，使用 MF 观测、线的单位方向向量和表面法向量来估计无漂移的旋转。然后，从点线跟踪恢复平移部分。在非-MW 场景中，跟踪和匹配的主导方向与点和线特征相结合，以估计完整的 6 自由度（DoF）相机姿势。此外，我们利用从多视角主导方向观测生成的旋转约束。约束与点和线的重投影误差相结合，通过局部地图束调整来细化相机姿势。在合成和真实世界数据集上的评估表明，我们的方法优于最先进的方法。在合成数据集上，平均定位精度为 1.5 厘米，与最先进的方法相当。在真实世界数据集上，平均定位精度为 1.7 厘米，比最先进的方法提高了 43%。我们的时间消耗减少了 36%。
